LumoraQ: Exploration of Quantum Machine Learning in Satellite Image Analysis

Core Information

• Original Author/Maintainer: BlackCherry
• Source Platform: GitHub
• Release Time: June 4, 2026

Project Objectives

Explore the application of quantum-classical hybrid machine learning methods on Earth observation data, using the EuroSAT satellite image dataset to verify the potential of quantum computing in remote sensing classification tasks. The current phase focuses on building a classical CNN baseline to provide a comparative benchmark for subsequent quantum models

EuroSAT Dataset

A widely used benchmark dataset in the remote sensing field:

• 27,000 satellite images
• 10 land cover categories: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake
• All images are uniformly resized to 64×64 pixels and normalized

Initial CNN Architecture

• Convolutional Layer 1: 3→16 filters (ReLU + Max Pooling)
• Convolutional Layer 2:16→32 filters (ReLU + Max Pooling)
• Fully Connected Layer1:8192→128
• Fully Connected Layer2:128→10
• Loss Function: Cross-entropy; Optimizer: Adam Result: Final accuracy of 80.28%

StrongCNN Upgrade

Improvements: Batch normalization, deeper convolutional architecture, Dropout regularization Data Augmentation: Random horizontal flip, rotation, color jitter Result: Accuracy increased to 88.83%

Training Observations

• Loss steadily decreased from 1.26 to 0.0791 with smooth convergence
• No signs of instability or divergence
• CNN effectively learns spatial patterns in satellite images

Benchmark Significance

The 88.83% accuracy of StrongCNN sets a performance threshold for quantum models

Next Phase Roadmap

• Install the PennyLane quantum framework
• Implement Variational Quantum Classifier (VQC)
• Compare with the StrongCNN baseline

Design Idea

A progressive approach ensures the classical component is optimized before introducing quantum components, enabling fair comparison

Core Value

• Provide a reproducible benchmark for quantum machine learning in the remote sensing field
• Demonstrate a progressive research path from classical baseline to quantum enhancement
• Offer data support for evaluating quantum advantage

Practical Reference

Provide a clear template for researchers working on the practical application of quantum machine learning